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Frontiers Technology of information and Computer (ICFTIC), ieee International conference on

作者： Yuxin Wang Zhile Jiang Wenrui Chen Chenyang Gao Kai Wu School of Biomedical Sciences and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The growing issue of adolescent myopia is mostly addressed through eye-focused methods, overlooking brain function's role. This study developed a toolbox using synchronized EEG and fNIRS to analyze brain activity in adolescents during 2D and 3D visual training. The system integrates data collection, preprocessing, feature extraction, and visualization, capturing metrics like power spectral density, correlation, phase locking, and hemoglobin levels. It is easy to use, employs simple algorithms, and offers clear visuals, helping to understand the impact of visual training on brain function.

关键词： Training Measurement visualization Three-dimensional displays Medical services Electroencephalography Synchronization Functional near-infrared spectroscopy Integrated circuit reliability Videos

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Reciprocal Twin Networks for Pedestrian Motion Learning and Future Path Prediction

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第3期32卷 1483-1497页

作者： Sun, Hao Zhao, Zhiqun Yin, Zhaozheng He, Zhihai Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

Modeling the moving behaviors and predicting the future paths of pedestrians, especially for those in complex scenes, remain a challenging problem in machine learning. We recognize that human motion trajectories, governed by social norms and constrained by physical structures of the surrounding environment, are both forward predictable and backward predictable. Motivated by this observation, we develop a new approach, called reciprocal twin networks, for human trajectory learning and prediction. We design two networks, a forward prediction network to predict future trajectory from past observations and a backward prediction that performs the trajectory prediction backward in time. The backward prediction network serves as the inverse operation of the forward prediction network, forming a reciprocal constraint. During the training stage, this reciprocal constraint allows them to be jointly learned for accurate and robust human trajectory prediction. During the inference stage, we borrow the concept of adversarial attack of deep neural networks, which iteratively modifies the input of the network to match the given or forced network output, and develop a new method, called reciprocal attack for matched prediction, to achieve accurate human trajectory prediction. Our experimental results on benchmark datasets demonstrate that our new method outperforms the state-of-the-art methods for human trajectory prediction.

关键词： Trajectory Predictive models Recurrent neural networks visualization Task analysis Semantics Generative adversarial networks Human trajectory prediction deep learning reciprocal learning reciprocal attack generative adversarial networks

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Optimizing Uncertainty Estimation on Scientific visualizations Using Learning Models

Optimizing Uncertainty Estimation on Scientific Visualizatio...

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2024 Workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Almeida, Clara J. Guerrero-Pantoja, David Pautsch, Erik Rizzi, Silvio Thiruvathukal, George K. Pantoja, Maria University of California Santa Cruz Robotics and Electrical Engineering Santa Cruz United States University of California Santa Cruz Computer Science and Game Design Santa Cruz United States Loyola University Chicago Computer Science Chicago United States Argonne National Laboratory Argonne Leadership Computing Facility Lemont United States Computer Science and Software Engineering California Polytechnics State University San Luis Obispo United States

ISBN: (纸本)9798350355543

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can lead to misclassifications where the model still produces high confidence scores, reflected in elevated softmax output values, even though its prediction is incorrect. Ideally, in such cases, the model should indicate uncertainty, effectively saying, "I do not know."In this work, we enhance the trustworthiness of DL models by quantifying their uncertainty and enabling selective classification, where the model abstains from making predictions when uncertainty is high. Instead of providing a single prediction, our model outputs a prediction distribution, allowing users to determine whether human intervention or other actions are necessary for handling high-uncertainty outputs. This paper evaluates uncertainty quantification across three different tasks: predicting airfoil pressure and velocity using a Reynolds-Averaged Navier-Stokes (RANS) model on a custom airfoil dataset, image classification using the ImageNet1K dataset, and digit recognition using the MNIST handwritten digits dataset. © 2024 ieee.

关键词： deep learning ensemble learning evidential learning scientific visualization uncertainty

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Enhancing Human-Robot Teleoperation through Depth Sensing and eXtended Reality

Enhancing Human-Robot Teleoperation through Depth Sensing an...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Shyam Ganatra Heejin Jeong Arizona State University

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Teleoperation enables the control of robots in remote, complex, or hazardous environments such as manufacturing, healthcare, and disaster response. However, traditional teleoperation systems lack immersive depth perception, which limits spatial awareness and precision. This paper presents a novel teleoperation framework integrating an Intel RealSense D345i depth camera with the Reachy humanoid robot, enhanced by augmented reality (AR) overlays in a virtual reality (VR) interface. By leveraging depth sensing, point cloud processing, and AR visualization, the system improves task accuracy, spatial awareness, and user experience. Our system utilizes advanced frameworks for seamless AR and VR integration. Preliminary findings demonstrate enhanced performance in tasks requiring precise object manipulation, with future user studies planned for quantitative evaluation.

关键词： Point cloud compression visualization Solid modeling Three-dimensional displays Robot vision systems Humanoid robots Medical services User experience Sensors Manufacturing

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Analysis and visualization of Time-Varying Harmonics in Transformer Inrush Currents

Analysis and Visualization of Time-Varying Harmonics in Tran...

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ieee International Instrumentation and Measurement Technology conference (I2MTC)

作者： Barros, Julio de Apraiz, Matilde Diego, Ramon, I Univ Cantabria Dept Comp Engn & Elect Santander Spain

ISBN: (纸本)9781728195391

The accurate measurement of the magnetizing inrush current in transformers is of the greatest interest for the correct operation and protection of power systems. This paper presents a method for analysis and visualization of time-varying harmonics using wavelets and its application to inrush currents in transformers. The Wavelet Packet Transform and the Inverse Wavelet Packet Transform are used as a filter bank for decomposition and reconstruction in the time domain of the harmonics in the inrush current. The peak and the r.m.s. magnitude and their decays, and the time evolution of harmonics in time and frequency domains as a function of the instant of connection of the input voltage to the primary winding are studied in detail. The results obtained can be used for the adequate design of the transformer protection and the selection of the instant of connection of voltage supply to the primary winding.

关键词： inrush current time-varying harmonics power quality transformers

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Election Data visualization in Virtual Reality Environment Using Oculus Quest 2

Election Data Visualization in Virtual Reality Environment U...

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International conference on Data and Software Engineering (ICODSE)

作者： Vania A. Qonita Latifa Dwiyanti School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Advancements in technology have led to the virtual reality development in Indonesia, impacting organizations and businesses positively. Virtual reality offers opportunities for data visualization in the upcoming election, providing insights into candidate wins and vote distribution. This paper develops a virtual reality election data visualization application using Unity and Oculus Quest 2. It includes data acquisition, modelling, rendering, and user interactions. Usability testing was conducted with eight users using task-based scenarios.

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Enhancing Environmental Data Insights with Oracle Analytics Cloud

Enhancing Environmental Data Insights with Oracle Analytics ...

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ieee International Scientific conference on Informatics

作者： Alenka Baggia Alenka Brezavšček Robert Leskovar Faculty of Organizational Sciences University of Maribor Kranj Slovenia

ISBN: (数字)9798350387681

ISBN: (纸本)9798350387698

In today's data-driven world, effective environmental management relies heavily on advanced analytics tools. This paper explores the application of Oracle Analytics Cloud for in-depth analysis and visualization of environmental data to improve decision-making processes in the municipality of Kranj. The study aims to use Oracle Analytics Cloud for processing and analysing large data sets collected by the municipality, with a focus on detecting trends and patterns that could support policymaking. Oracle Analytics Cloud enables the integration of various data sources and provides a comprehensive platform for cross-functional environmental analysis.

关键词： Data analysis Atmospheric measurements Decision making Data visualization Transforms Strategic planning Air quality Market research Pollution measurement Informatics

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MLN-Visualizer: Interactive visualization of Graphs and Multilayer Network Analysis Results

MLN-Visualizer: Interactive Visualization of Graphs and Mult...

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ieee International conference on Big Data Computing Service and Applications (BigDataService)

作者： Viraj Sabhaya Amey Shinde Abhishek Santra Sharma Chakravarthy IT Lab and CSE Department UT Arlington Arlington Texas

Big data analysis is broader with a life cycle from choosing a model to represent data, generation of the model for the given data set, analysis using operations relevant to the model, and finally, visualization to understand the results. The ability to perform this end-to-end workflow has become more important as the data size and complexity have increased significantly. visualization is especially important when the data sizes are large and models are complex (e.g., graphs, multilayer networks (MLNs)) which is difficult to comprehend without *** this poster paper, we describe a web-based tool MLN-Visualizer for visualizing graphs and MLN analysis results. Interactively, the user can visualize their uploaded input data and analysis results, in multiple ways, using a menu-driven interface. We hint at adding privacy to the resulting visualization.

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OpenSceneVLAD: Appearance Invariant, Open Set Scene Classification 39

OpenSceneVLAD: Appearance Invariant, Open Set Scene Classifi...

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ieee International conference on Robotics and Automation (ICRA)

作者： Smith, William H. B. Milford, Michael McDonald-Maier, Klaus D. Ehsan, Shoaib Fisher, R. B. Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Queensland Univ Thchnol Sch Elect Engn & Comp Sci Brisbane Qld 4000 Australia Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland

ISBN: (纸本)9781728196817

Scene classification is a well-established area of computer vision research that aims to classify a scene image into pre-defined categories such as playground, beach and airport. Recent work has focused on increasing the variety of pre-defined categories for classification, but so far failed to consider two major challenges: changes in scene appearance due to lighting and open set classification (the ability to classify unknown scene data as not belonging to the trained classes). Our first contribution, SceneVLAD, fuses scene classification and visual place recognition CNNs for appearance invariant scene classification that outperforms state-of-the-art scene classification by a mean F1 score of up to 0.1. Our second contribution, OpenSceneVLAD, extends the first to an open set classification scenario using intra-class splitting to achieve a mean increase in F1 scores of up to 0.06 compared to using state-of-the-art openmax layer. We achieve these results on three scene class datasets extracted from large scale outdoor visual localisation datasets, one of which we collected ourselves.

关键词： visualization Computer vision Image analysis Automation Fuses Lighting Airports

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Reconstruction and visualization of Protein Structures by exploiting Bidirectional Neural Networks and Discrete Classes 25

Reconstruction and Visualization of Protein Structures by ex...

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25th International conference information Visualisation (IV) - AI and Visual Analytics and Data Science

作者： Citarella, Alessia Auriemma Porcelli, Lorenzo Di Biasi, Luigi Risi, Michele Tortora, Genoveffa Univ Salerno Dept Comp Sci Fisciano SA Italy

ISBN: (纸本)9781665438278

In recent years, Deep Learning techniques have achieved some success in bioinformatics tasks, including protein conformation prediction. In this work, we propose a Bidirectional Long Short-Term Memory (BLSTM) network system, called Human Proteins Angles Prediction (HPAP), in order to improve the prediction of dihedral angles of proteins. We have introduced a discrete subdivision in classes of 5 degrees for protein torsion angles and four new features related to accessible surface area and volume. In total there are 73 classes (72 classes include the angles between -180 degrees and 180 degrees, a further class is used to code the free angles at the beginning of the sequence) with a maximum expected error of +/- 2.5 degrees. We have tested three model variants in several parameter combinations. With our model, we have obtained a decrease of the mean absolute error of about 2 degrees for the psi angle. Although our dataset is reduced in size, the accuracy of phi and psi angles is comparable to the existing methods. Predicting angles accurately is useful for accurately reconstructing the three-dimensional structure of a protein. In this context, the prediction is limited to the phi and psi angles and we will visualize what happens locally when a prediction is correct. In case the prediction is far from true angles, even a small error can deconstruct the backbone.

关键词： Protein Structure Torsion Angle Prediction Long Short-Term Memory Deep Learning

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